Purpose To compare image quality, patient preparation time, and radiation dose using a single axial rotation with 16-cm wide-detector computed tomography (CT) in imaging the infant chest without sedation with those in infants examined by using a 64-row CT and sedation. Materials and Methods Thirty-two infants (group 1) were prospectively enrolled to undergo nonenhanced chest CT without sedation using a single axial rotation on a 16-cm wide-detector CT scanner. Patients were imaged with automatic tube current modulation and tube voltages of 80 kVp for patients weighing 5 kg or less and 100 kVp for patients weighing more than 5 kg. Patient preparation time, CT dose index (CTDI), dose-length product (DLP), and image quality were compared with those in a historical control group consisting of 30 infants (group 2) who underwent conventional helical scanning with sedation performed by using a 64-row volume CT scanner. The Student t test for independent samples was used to assess continuous variables. The Mann-Whitney rank test and the κ test were used to evaluate image quality. Results There was no statistically significant difference in body weight, age, mean CT attenuation value, image noise, and subjective image quality score between the two groups. However, compared with the group scanned by using a 64-row volume CT scanner (group 2), group 1 experienced significantly reduced scan time by 83% (0.35 second vs 2.01 seconds ± 0.21 [standard deviation]), preparation time by 57% (41.25 minutes ± 103.78 vs 96.5 minutes ± 151.77), CTDI by 42% (2.03 mGy ± 0.4 vs 3.52 mGy ± 0.03), and DLP by 52% (27.07 mGy·cm ± 6.97 vs 55.84 mGy·cm ± 6.46) (P< .05 for all). Conclusion Compared with conventional 64-row helical CT with sedation, use of a single axial rotation with 16-cm wide-detector CT in imaging the infant chest without sedation can reduce radiation dose, preparation time, and total scan time, while providing comparable image quality. RSNA, 2017.
Background and Objectives: Along with the rapid improvement of imaging technology, convex probe endobronchial ultrasound (CP-EBUS) sonographic features play an increasingly important role in the diagnosis of intrathoracic lymph nodes (LNs). Conventional qualitative and quantitative methods for EBUS multimodal imaging are time-consuming and rely heavily on the experience of endoscopists. With the development of deep-learning (DL) models, there is great promise in the diagnostic field of medical imaging. Materials and Methods: We developed DL models to retrospectively analyze CP-EBUS images of 294 LNs from 267 patients collected between July 2018 and May 2019. The DL models were trained on 245 LNs to differentiate benign and malignant LNs using both unimodal and multimodal CP-EBUS images and independently evaluated on the remaining 49 LNs to validate their diagnostic efficiency. The human comparator group consisting of three experts and three trainees reviewed the same test set as the DL models. Results: The multimodal DL framework achieves an accuracy of 88.57% (95% confidence interval [CI] [86.91%–90.24%]) and area under the curve (AUC) of 0.9547 (95% CI [0.9451–0.9643]) using the three modes of CP-EBUS imaging in comparison to the accuracy of 80.82% (95% CI [77.42%–84.21%]) and AUC of 0.8696 (95% CI [0.8369–0.9023]) by experts. Statistical comparison of their average receiver operating curves shows a statistically significant difference (P < 0.001). Moreover, the multimodal DL framework is more consistent than experts (kappa values 0.7605 vs. 0.5800). Conclusions: The DL models based on CP-EBUS imaging demonstrated an accurate automated tool for diagnosis of the intrathoracic LNs with higher diagnostic efficiency and consistency compared with experts.
With the rise of artificial magnetism and metamaterials, toroidal resonance has gained much attention for its special properties. In this paper, we propose a novel hybrid graphene-metal metamolecule consisting of a square bracket-like resonator and two asymmetric U-shaped resonators. By applying various Fermi energies to graphene, the amplitude of electromagnetically induced transparency (EIT) can be efficiently manipulated, and the maximum amplitude modulation depth can attain 81% in the microwave region. Numerical simulations and theoretical analysis demonstrate that the dynamic manipulation is mainly induced by the active tuning toroidal resonance through the recombination effect of the conductive graphene. Also, the maximum group delay of 85 ps can be attained and controlled with the increasing Fermi energy. The proposed hybrid graphene-metal metamolecule and dynamically manipulating mode presents a novel modulating strategy of EIT-like analog based on the toroidal response, which has great application for the design of efficient tunable resonators, filters, and sensors.
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